THE SOURCE
PDF inbox
→Invoices, contracts, exposés, reports, scans — any PDF up to 32 MB and 100 pages. Drop in or pipe in via API; the engine handles the rest.
07 — DOCUMENT AI
Every PDF,
read
and ready.
Drop a PDF. A four-pass validation pipeline returns clean JSON with a confidence score on every field — ~90% accuracy on the messiest formats.
The problem
PDFs piled up. Nobody read them.
Every team has the same pile. Invoices that need to be filed. Contracts that hide the terms that matter. Property exposés where the only number you need is buried on page 47. Government forms, claim documents, vendor scans — all of it carrying the data your stack actually needs to run. And all of it stuck inside a PDF that nobody has time to read carefully, never mind type into a system. So ops re-types. Sales re-types. Finance re-types. Three teams paying for the same lossy translation.
- — INVOICES piled up unfiled, line items typed by hand into sevDesk.
- — CONTRACTS with key terms living in a spreadsheet nobody trusts.
- — REPORTS eighty pages, three tables, zero usable data.
- — EXPOSÉS three competing properties, one Excel sheet, one weekend gone.
Every PDF was data prison.
The shift
Drop in.Cleaned out.Verified.
A single LLM call against a PDF hallucinates somewhere around a third of the time. Off-the-shelf OCR loses the layout. Document-AI vendors give you confidence scores but not corrections. None of them are good enough to ship the output straight into your CRM or accounting system.
The shift is the pipeline. Extract once. Then re-read the document to verify every field against the source. Then run a correction pass that removes fabricated fields and fixes the ones the verifier flagged. Then a programmatic arithmetic pass that catches sums that don't add up. The same model the single-shot approach would use, run four times under structured pressure — and the accuracy goes from ~65% to ~90% on the same input.
Layout handled by a dedicated OCR model. Validation handled by the LLM. Schema enforced by tool-calling. You get clean JSON, every field source-tagged back to the page it came from.
What it reads
Anything PDF.
Adaptive extraction means the engine first classifies what it's looking at, then runs the schema that fits. One stack, every document type — including the scanned and the multi-language and the legacy.
- ·INVOICESLine items, VAT, totals, due dates, vendor identity — every field traced back to its page.
- ·CONTRACTSParties, dated obligations, exit clauses, indexation rules. Surfaces the language that matters.
- ·EXPOSÉSYield, occupancy, rent roll, asset class, WAULT — built for real-estate dealflow.
- ·REPORTSMulti-column layouts, tables, footnotes — including the 80-pager nobody opens.
- ·FORMSGovernment forms, intake sheets, claim documents — fixed schema or freeform.
- ·SCANSBad photocopies, low-res scans, mixed handwriting. Layout-aware OCR ships with the stack.
How it works
Four passes per page.
Pass one extracts every field with source attribution. Pass two re-reads the document to verify each one. Pass three corrects what the verifier flagged and strips out fabrications. Pass four runs arithmetic checks — totals, ratios, dates that don't fit. What lands in your stack has been agreed upon four times.
“Verified by an agent.that’s the whole product
Integrations
Lands inyour stack.
Sheets, CRM, accounting, warehouse, webhook — pick the destination and the engine writes the structured output where you already work. Every field carries its confidence score; you choose the threshold for auto-accept versus review.
SHEETS · EXCEL
Spreadsheet
→Extracted fields land in Google Sheets or Excel as clean rows — one document per row, columns mapped to your schema.
HUBSPOT · PIPEDRIVE
CRM
→New leads, properties, vendors — created or enriched directly in your CRM with every field source-tagged back to the original page.
SEVDESK · XERO
Accounting
→Invoice line items, VAT splits, due dates, vendor identity — drafted as bookings in sevDesk, Xero or QuickBooks, ready for review.
POSTGRES · SQL
Database
→For volume work, the verified JSON streams straight into your warehouse — Postgres, BigQuery, custom schema. Confidence scores included.
CONNECT ANYWHERE
Webhook
→POST the structured output to any endpoint — your app, n8n, Zapier, an internal service. Every payload includes confidence and source.
Pick a destination. Or two. The engine writes where your team already works.
The side effect
Ops
stops typing.
The half-week that finance and ops spent re-typing PDFs into systems goes back. New documents land structured, source-tagged, ready for the next step. The team reviews edge cases instead of typing line items. And the deals, leads and properties that lived inside unread PDFs are suddenly searchable, dashboarded, alerted on — the things you were already paying for the data to do.
~90%
Field accuracy out of the box
0 retyping
Hours per week
How it lands
We schema. We seed. It feeds.
01 · SCHEMA
Day one.
We define the JSON schema together — the fields you actually need, the validation rules, the confidence threshold for auto-accept. The engine renders against your schema, not a generic one.
02 · SEED
Week one.
We run your back-catalogue of PDFs through the pipeline, tune the prompts against your worst-case documents, and wire the output into your first destination — sheets, CRM, accounting, or warehouse.
03 · FEEDS
Week two onward.
New PDFs land via inbox, upload or API. The pipeline runs. Verified data lands in your stack. We're around when you want a new destination wired in or a tighter schema.
/ Ready when you are
See your PDFs come out clean
Twenty minutes. Bring a sample PDF — we'll walk through the schema, the validation passes, and where the JSON should land.
Or email matthias@lifeisapitch.io